code: def _size(self):
      if settings.debug.on():
          if hasattr(self.kernel, "size"):
              raise RuntimeError(
                  "Kernels must define `num_outputs_per_input` and should not define `size`"
              )
  
      x1 = self.x1
      x2 = self.x2
      num_outs_per_in = self.kernel.num_outputs_per_input(x1, x2)
      if isinstance(num_outs_per_in, tuple):
          num_outs_per_in_rows, num_outs_per_in_cols = num_outs_per_in
      else:
          num_outs_per_in_rows = num_outs_per_in
          num_outs_per_in_cols = num_outs_per_in
      num_rows = x1.size(-2) * num_outs_per_in_rows
      num_cols = x2.size(-2) * num_outs_per_in_cols
  
      # Default case - when we're not using broadcasting
      # We write this case special for efficiency
      if (
          x1.shape[:-2] == x2.shape[:-2]
          and x1.shape[:-2] == self.kernel.batch_shape
      ):
          expected_size = self.kernel.batch_shape + torch.Size(
              (num_rows, num_cols)
          )
  
      # When we're using broadcasting
      else:
          try:
              if x1.size(-1) != x2.size(-1):
                  raise RuntimeError
  
              expected_size = torch.broadcast_shapes(
                  x1.shape[:-2], x2.shape[:-2], self.kernel.batch_shape
              ) + torch.Size([num_rows, num_cols])
  
          except RuntimeError:
              raise RuntimeError(
                  f"x1 and x2 were not broadcastable with kernel of batch_shape {self.kernel.batch_shape}. "
                  f"Got x1.shape = {x1.shape} and x2.shape = {x2.shape}"
              )
  
      # Handle when the last dim is batch
      if self.last_dim_is_batch:
          expected_size = (
              expected_size[:-2] + x1.shape[-1:] + expected_size[-2:]
          )
      return expected_size
  
imports: import functools
  from typing import Callable
  import torch
  from linear_operator import LinearOperator, to_linear_operator
  from linear_operator.utils.getitem import _noop_index
  from ..None import beta_features, settings
  from ..utils import deprecation
  from ..utils.memoize import cached
label_desc: Use image node_id 11 for calling the LazyEvaluatedKernelTensor obj's underlying member method code with example usage: obj._size() and returns: expected_size